Computational Biology in the Discovery of Biomarkers in the Diagnosis, Treatment and Management of Cardiovascular Diseases.

Cardiology and cardiovascular medicine Pub Date : 2024-01-01 Epub Date: 2024-09-05
Irene Batta, Ritika Patial, Ranbir C Sobti, Devendra K Agrawal
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Abstract

Cardiovascular diseases are the leading cause of mortality worldwide, with a disproportionately high burden in low- and middle-income countries. Biomarkers play a crucial role in the early detection, diagnosis, and treatment of cardiovascular diseases by providing valuable insights into the normal and abnormal conditions of the heart and vascular system. The biomarkers derived from the cells and tissues can be identified and quantified in the blood and other body fluids and in tissues. Changes in their expression level under a pathological condition provide clinical information on the underlying pathophysiology that could have predictive, diagnostic, and prognostic value in the treatment of a disease process, and therefore incorporated in clinical guidelines. This enhances the effectiveness of biomarkers in risk stratification and therapeutic decisions in personalized medicine and improvement in patient outcomes. Biomarkers could be protein, carbohydrate, or genome-based and may also be derived from lipids and nucleic acids. Computational biology has emerged as a powerful discipline in biomarker discovery, leveraging computational techniques to identify and validate biological markers for disease diagnosis, prognosis, and drug response prediction. The convergence of advanced technologies, such as artificial intelligence, multi-omics profiling, liquid biopsies, and imaging, has led to a significant shift in the discovery and development of biomarkers, enabling the integration of data from multiple biological scales and providing a more comprehensive understanding of the complex signaling and transcriptional networks underlying disease pathogenesis. In this article, we reviewed the role of computational biology integrated with genomics, proteomics, and metabolomics, together with machine learning techniques and predictive modeling and data integration in the discovery of biomarkers in cardiovascular diseases. We discussed specific biomarkers, including epigenetic, metabolic, and emerging biomarkers, such as extracellular vesicles, miRNAs, and circular RNAs, and their role in the pathophysiology of the heart and vascular diseases.

在心血管疾病诊断、治疗和管理中发现生物标记物的计算生物学。
心血管疾病是导致全球死亡的主要原因,在中低收入国家的负担尤为沉重。生物标志物可提供有关心脏和血管系统正常和异常状况的宝贵信息,在心血管疾病的早期检测、诊断和治疗方面发挥着至关重要的作用。从细胞和组织中提取的生物标记物可以在血液、其他体液和组织中进行鉴定和量化。它们在病理状态下的表达水平变化可提供有关潜在病理生理学的临床信息,对疾病过程的治疗具有预测、诊断和预后价值,因此可纳入临床指南。这提高了生物标志物在个性化医疗的风险分层和治疗决策中的有效性,并改善了患者的预后。生物标志物可以是蛋白质、碳水化合物或基因组,也可以来自脂质和核酸。计算生物学已成为生物标记物发现领域的一门强大学科,它利用计算技术来识别和验证用于疾病诊断、预后和药物反应预测的生物标记物。人工智能、多组学剖析、液体活检和成像等先进技术的融合,使生物标记物的发现和开发发生了重大转变,实现了多种生物尺度数据的整合,并使人们对疾病发病机制背后复杂的信号转导和转录网络有了更全面的了解。在本文中,我们回顾了计算生物学与基因组学、蛋白质组学和代谢组学的整合,以及机器学习技术、预测建模和数据整合在发现心血管疾病生物标志物中的作用。我们讨论了具体的生物标志物,包括表观遗传、代谢和新兴生物标志物,如细胞外囊泡、miRNA 和环状 RNA,以及它们在心脏和血管疾病的病理生理学中的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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